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De novo identification and quantification of single amino-acid variants in human brain. | LitMetric

De novo identification and quantification of single amino-acid variants in human brain.

J Mol Cell Biol

Key Laboratory of Systems Biology, Chinese Academy of Sciences, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Shanghai 200031, China

Published: October 2014

AI Article Synopsis

  • The study introduces a new method for detecting single amino-acid variants (SAVs) in proteins without relying on existing SNP databases, using a mass spectrometry technique for sequencing.
  • It identifies peptide candidates and compares them to a theoretical protein database to generate SAVs while controlling for errors through database re-searching.
  • By combining this method with DNA/RNA sequencing, the research confirms various origins of SAVs and reveals distinct expressions across different brain tissues, highlighting the method's potential for identifying clinical biomarkers.

Article Abstract

The detection of single amino-acid variants (SAVs) usually depends on single-nucleotide polymorphisms (SNPs) database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. In human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.

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Source
http://dx.doi.org/10.1093/jmcb/mju031DOI Listing

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